"""Contains definitions for the preactivation form of Residual Networks.

Residual networks (ResNets) were originally proposed in:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385

The full preactivation 'v2' ResNet variant implemented in this module was
introduced by:
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Identity Mappings in Deep Residual Networks. arXiv: 1603.05027

The key difference of the full preactivation 'v2' variant compared to the
'v1' variant in [1] is the use of batch normalization before every weight layer
rather than after.
"""















from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

_BATCH_NORM_DECAY = 0.997
_BATCH_NORM_EPSILON = 1e-05

_SEED = 7
tf.set_random_seed(_SEED)

def batch_norm_relu(inputs, is_training, data_format):
    """Performs a batch normalization followed by a ReLU."""
    
    
    inputs = tf.layers.batch_normalization(inputs=
    inputs, axis=1 if data_format == 'channels_first' else 3, momentum=
    _BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True, scale=True, training=
    is_training, fused=True)
    inputs = tf.nn.relu(inputs)
    return inputs


def fixed_padding(inputs, kernel_size, data_format):
    """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                 Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
  """
    pad_total = kernel_size - 1
    pad_beg = pad_total // 2
    pad_end = pad_total - pad_beg
    
    if data_format == 'channels_first':
        padded_inputs = tf.pad(inputs, [[0, 0], [0, 0], [
        pad_beg, pad_end], [pad_beg, pad_end]])
    else:
        padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [
        pad_beg, pad_end], [0, 0]])
    return padded_inputs


def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
    """Strided 2-D convolution with explicit padding."""
    
    
    if strides > 1:
        inputs = fixed_padding(inputs, kernel_size, data_format)
    
    return tf.layers.conv2d(inputs=
    inputs, filters=filters, kernel_size=kernel_size, strides=strides, padding='SAME' if 
    strides == 1 else 'VALID', use_bias=False, kernel_initializer=
    tf.variance_scaling_initializer(), data_format=
    data_format)


def building_block(inputs, filters, is_training, projection_shortcut, strides, data_format):
    """Standard building block for residual networks with BN before convolutions.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    filters: The number of filters for the convolutions.
    is_training: A Boolean for whether the model is in training or inference
      mode. Needed for batch normalization.
    projection_shortcut: The function to use for projection shortcuts (typically
      a 1x1 convolution when downsampling the input).
    strides: The block's stride. If greater than 1, this block will ultimately
      downsample the input.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    The output tensor of the block.
  """
    
    shortcut = inputs
    inputs = batch_norm_relu(inputs, is_training, data_format)
    
    
    
    if projection_shortcut is not None:
        shortcut = projection_shortcut(inputs)
    
    inputs = conv2d_fixed_padding(inputs=
    inputs, filters=filters, kernel_size=3, strides=strides, data_format=
    data_format)
    
    inputs = batch_norm_relu(inputs, is_training, data_format)
    inputs = conv2d_fixed_padding(inputs=
    inputs, filters=filters, kernel_size=3, strides=1, data_format=
    data_format)
    
    return inputs + shortcut


def bottleneck_block(inputs, filters, is_training, projection_shortcut, strides, data_format):
    """Bottleneck block variant for residual networks with BN before convolutions.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    filters: The number of filters for the first two convolutions. Note that the
      third and final convolution will use 4 times as many filters.
    is_training: A Boolean for whether the model is in training or inference
      mode. Needed for batch normalization.
    projection_shortcut: The function to use for projection shortcuts (typically
      a 1x1 convolution when downsampling the input).
    strides: The block's stride. If greater than 1, this block will ultimately
      downsample the input.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    The output tensor of the block.
  """
    
    shortcut = inputs
    inputs = batch_norm_relu(inputs, is_training, data_format)
    
    
    
    if projection_shortcut is not None:
        shortcut = projection_shortcut(inputs)
    
    inputs = conv2d_fixed_padding(inputs=
    inputs, filters=filters, kernel_size=1, strides=1, data_format=
    data_format)
    
    inputs = batch_norm_relu(inputs, is_training, data_format)
    inputs = conv2d_fixed_padding(inputs=
    inputs, filters=filters, kernel_size=3, strides=strides, data_format=
    data_format)
    
    inputs = batch_norm_relu(inputs, is_training, data_format)
    inputs = conv2d_fixed_padding(inputs=
    inputs, filters=4 * filters, kernel_size=1, strides=1, data_format=
    data_format)
    
    return inputs + shortcut


def block_layer(inputs, filters, block_fn, blocks, strides, is_training, name, data_format):
    """Creates one layer of blocks for the ResNet model.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or
      [batch, height_in, width_in, channels] depending on data_format.
    filters: The number of filters for the first convolution of the layer.
    block_fn: The block to use within the model, either `building_block` or
      `bottleneck_block`.
    blocks: The number of blocks contained in the layer.
    strides: The stride to use for the first convolution of the layer. If
      greater than 1, this layer will ultimately downsample the input.
    is_training: Either True or False, whether we are currently training the
      model. Needed for batch norm.
    name: A string name for the tensor output of the block layer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    The output tensor of the block layer.
  """
    
    
    filters_out = 4 * filters if block_fn is bottleneck_block else filters
    
    def projection_shortcut(inputs):
        return conv2d_fixed_padding(inputs=
        inputs, filters=filters_out, kernel_size=1, strides=strides, data_format=
        data_format)
    
    
    inputs = block_fn(inputs, filters, is_training, projection_shortcut, strides, 
    data_format)
    
    for _ in range(1, blocks):
        inputs = block_fn(inputs, filters, is_training, None, 1, data_format)
    
    return tf.identity(inputs, name)


def cifar10_resnet_v2_generator(resnet_size, num_classes, data_format=None):
    """Generator for CIFAR-10 ResNet v2 models.

  Args:
    resnet_size: A single integer for the size of the ResNet model.
    num_classes: The number of possible classes for image classification.
    data_format: The input format ('channels_last', 'channels_first', or None).
      If set to None, the format is dependent on whether a GPU is available.

  Returns:
    The model function that takes in `inputs` and `is_training` and
    returns the output tensor of the ResNet model.

  Raises:
    ValueError: If `resnet_size` is invalid.
  """
    if resnet_size % 6 != 2:
        raise ValueError('resnet_size must be 6n + 2:', resnet_size)
    
    num_blocks = resnet_size - 2 // 6
    
    if data_format is None:
        data_format = 'channels_first' if 
        tf.test.is_built_with_cuda() else 'channels_last'
    
    def model(inputs, is_training):
        """Constructs the ResNet model given the inputs."""
        if data_format == 'channels_first':
            
            
            
            inputs = tf.transpose(inputs, [0, 3, 1, 2])
        
        inputs = conv2d_fixed_padding(inputs=
        inputs, filters=16, kernel_size=3, strides=1, data_format=
        data_format)
        inputs = tf.identity(inputs, 'initial_conv')
        
        inputs = block_layer(inputs=
        inputs, filters=16, block_fn=building_block, blocks=num_blocks, strides=1, is_training=
        is_training, name='block_layer1', data_format=
        data_format)
        inputs = block_layer(inputs=
        inputs, filters=32, block_fn=building_block, blocks=num_blocks, strides=2, is_training=
        is_training, name='block_layer2', data_format=
        data_format)
        inputs = block_layer(inputs=
        inputs, filters=64, block_fn=building_block, blocks=num_blocks, strides=2, is_training=
        is_training, name='block_layer3', data_format=
        data_format)
        
        inputs = batch_norm_relu(inputs, is_training, data_format)
        inputs = tf.layers.average_pooling2d(inputs=
        inputs, pool_size=8, strides=1, padding='VALID', data_format=
        data_format)
        inputs = tf.identity(inputs, 'final_avg_pool')
        inputs = tf.reshape(inputs, [(-1), 64])
        inputs = tf.layers.dense(inputs=inputs, units=num_classes)
        inputs = tf.identity(inputs, 'final_dense')
        return inputs
    
    return model


def imagenet_resnet_v2_generator(block_fn, layers, num_classes, data_format=None):
    """Generator for ImageNet ResNet v2 models.

  Args:
    block_fn: The block to use within the model, either `building_block` or
      `bottleneck_block`.
    layers: A length-4 array denoting the number of blocks to include in each
      layer. Each layer consists of blocks that take inputs of the same size.
    num_classes: The number of possible classes for image classification.
    data_format: The input format ('channels_last', 'channels_first', or None).
      If set to None, the format is dependent on whether a GPU is available.

  Returns:
    The model function that takes in `inputs` and `is_training` and
    returns the output tensor of the ResNet model.
  """
    
    if data_format is None:
        data_format = 'channels_first' if 
        tf.test.is_built_with_cuda() else 'channels_last'
    
    def model(inputs, is_training):
        """Constructs the ResNet model given the inputs."""
        if data_format == 'channels_first':
            
            
            inputs = tf.transpose(inputs, [0, 3, 1, 2])
        
        inputs = conv2d_fixed_padding(inputs=
        inputs, filters=64, kernel_size=7, strides=2, data_format=
        data_format)
        inputs = tf.identity(inputs, 'initial_conv')
        inputs = tf.layers.max_pooling2d(inputs=
        inputs, pool_size=3, strides=2, padding='SAME', data_format=
        data_format)
        inputs = tf.identity(inputs, 'initial_max_pool')
        
        inputs = block_layer(inputs=
        inputs, filters=64, block_fn=block_fn, blocks=layers[0], strides=1, is_training=
        is_training, name='block_layer1', data_format=
        data_format)
        inputs = block_layer(inputs=
        inputs, filters=128, block_fn=block_fn, blocks=layers[1], strides=2, is_training=
        is_training, name='block_layer2', data_format=
        data_format)
        inputs = block_layer(inputs=
        inputs, filters=256, block_fn=block_fn, blocks=layers[2], strides=2, is_training=
        is_training, name='block_layer3', data_format=
        data_format)
        inputs = block_layer(inputs=
        inputs, filters=512, block_fn=block_fn, blocks=layers[3], strides=2, is_training=
        is_training, name='block_layer4', data_format=
        data_format)
        
        inputs = batch_norm_relu(inputs, is_training, data_format)
        inputs = tf.layers.average_pooling2d(inputs=
        inputs, pool_size=7, strides=1, padding='VALID', data_format=
        data_format)
        inputs = tf.identity(inputs, 'final_avg_pool')
        inputs = tf.reshape(inputs, [1, 512 if 
        block_fn is building_block else 2048])
        inputs = tf.layers.dense(inputs=inputs, units=num_classes)
        inputs = tf.identity(inputs, 'final_dense')
        return inputs
    
    return model


def imagenet_resnet_v2(resnet_size, num_classes, data_format=None):
    """Returns the ResNet model for a given size and number of output classes."""
    model_params = {18: {'block': 
    building_block, 'layers': [2, 2, 2, 2]}, 34: {'block': 
    building_block, 'layers': [3, 4, 6, 3]}, 50: {'block': 
    bottleneck_block, 'layers': [3, 4, 6, 3]}, 101: {'block': 
    bottleneck_block, 'layers': [3, 4, 23, 3]}, 152: {'block': 
    bottleneck_block, 'layers': [3, 8, 36, 3]}, 200: {'block': 
    bottleneck_block, 'layers': [3, 24, 36, 3]}}
    
    
    if resnet_size not in model_params:
        raise ValueError('Not a valid resnet_size:', resnet_size)
    
    params = model_params[resnet_size]
    return imagenet_resnet_v2_generator(
    params['block'], params['layers'], num_classes, data_format)